This blending process can create domain- or context-specific data that can be a huge benefit to users, Frankle adds. “It can be very powerful, because it can help you get exactly the right data you want, exactly the right, behaviors, properties, and shape of data you want,” he adds.
One good use of synthetic data would be to train autonomous cars when they need to hit the brakes, Mostly AI’s Ebert says. Instead of filming millions of hours of video showing multiple weather conditions, obstacles, and other potential variables, car makers can use synthetically generated visuals to mimic real-world conditions.
“We can use seed data, so some videos of rabbits or kids or whatever you want to train on, allowing us to create these millions of distinct examples which are still realistic,” she says.
